Graph Compression, Neural Networks, and More – Using Oscillators to Compute

Wilkie Olin-Ammentorp, Argonne National Laboratory
CS Seminar Graphic

The rapid advancement of digital electronic logic has significantly improved computing capabilities over the past few decades. However, continuing this progress by scaling down devices has become increasingly difficult and costly. This has led to a renewal of interest in alternative materials, devices, and computing strategies. I present an overview of one promising computing strategy which utilizes oscillatory elements to express and compute with information represented via phases. These systems can compute numerous functions such as state machines, neural networks, and graph compression via novel hardware elements. This provides a strategy for emerging hardware devices with oscillatory properties to be integrated into computing systems to accelerate or improve the efficiency of certain tasks.

Dr. Wilkie Olin-Ammentorp is a postdoctoral researcher in the Mathematics & Computer Science Division of the Argonne National Laboratory. He completed his PhD in Nanoengineering at the State University of New York Polytechnic Institute in 2019 and went on to a first post-doctoral appointment at the University of California, San Diego in the Department of Medicine before joining Argonne in 2022. His research focuses on applying co-design and novel computing strategies to improve the throughput and efficiency of future scientific computing systems.

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